On Variations of the Absolute Percent Error For Lumpy and Intermittent Data - Pipon, Dave Elsisura; Associate Professor Julian A. Cayton, Phd (2024)
ABSTRACT

Preponderance of zeroes are often present in business, social science, and ecological data. These are define as lumpy and intermittent data series characterized by their magnitude intermittency and erraticness. Against this backdrop, one of the commonly used forecast measure, the Mean absolute percentage error (MAPE), its perennial challenge with infinite of undefines result from actual value of zero become frequent. In line with this, the main objective of the study is to devise forecast error measures that replace MAPE, while maintaining its advantages, specifically, scale-independency and interpretability. Taken from the Gudermannian absolute percentage error (MGAPE), Mean absolute Sigmoid absolute percentage error (MASAPE), and Mean square root Sigmoid absolute percentage error (MSRSAPE) were proposed. These devised forecast error measure outperform MAPE, address completely the event of an infinity and partially address its asymmetry while maintaining it interpretability. Among the advised forecast error measures, MSRASAPE emerged to have the best properties across aspects followed by MASAPE. In general, it is recommended to use MSRSAPE in lumpy and intermittent data and treating pair of actual and forecast zero values as zero error. Conversely, if there is sufficient reason to cater outliers, MASAPE is the suggested alternative.
Includes bibliographic references.
Keywords: Lumpy data -- Intermittent data -- Mean absolute percentage error -- Forecast measure -- Forecast method selection